Pharma Companies United By a Need for Common Data Models

Posted on October 18th, 2017 by Betsy Davis in Pharma R&D

There is nothing that pharmaceutical companies guard more fiercely than their intellectual property. Devising and patenting the next big wonder drug is every company’s dream, and the revenue generated from those cherished successes help fund the research that leads to future innovations. But sometimes it is worth it for major firms to let down their guard a bit to consider areas where knowledge sharing might do them—and the entire scientific community—more good than harm. That’s the idea behind The Pistoia Alliance, which has brought together heavy-hitters like GSK, AstraZeneca and Roche for pre-competitive collaboration around common R&D obstacles.

Nowadays, this kind of cooperation is especially important where data is concerned. In the age of big data, the amount of available information is multiplying by the minute. Data is everywhere, coming from numerous sources in a variety of formats. If this valuable data is to be effectively managed and used by life science organizations, it’s important that we start seeing more standardization. Common models that allow for the integration of data will be better for everybody.

Read this article to find out how Elsevier has been helping the industry overcome some data-related challenges by donating its Unified Data Model to The Pistoia Alliance.

---

 

All opinions shared in this post are the author’s own.

R&D Solutions for Pharma & Life Sciences

We're happy to discuss your needs and show you how Elsevier's Solution can help.

Contact Sales

Betsy Davis
Senior Strategic Marketing Manager

Connect on LinkedIn
Follow on Twitter
https://elsevier.com

Related posts

• Life Sciences trends that make me excited for 2020 (and beyond)
• What were the most important lessons in Life Sciences in 2019?
• Data-Driven Technologies are Shaking Up the Drug Development Process
• Pharma Industry Players Work Together to Raise Awareness and Find Cures for Rare Diseases
• Machine Learning Could Make Drug Discovery Faster, Cheaper, Better